The United Nations publishes updated estimates and projections of the populations of all the world's countries, broken down by age and sex. These are widely used by international organizations, governments, the private sector and researchers, for example for climate modeling and for assessing progress towards the Millenium Development Goals. The UN's current projections are deterministic, but assessing uncertainty about population estimates and projections is important for policy-making and other purposes. We propose to develop a fully probabilistic population projection methodology. We will develop methods for probabilistic projection of fertility and mortality, taking account of within-country and between-country correlations. We will develop methods for probabilistic projection of international migration. We will develop methods for probabilistic population projections in countries with generalized sexually transmitted infectious disease epidemics, which require special methods because the demographic impact of such diseases is massive and different from most other diseases, being concentrated among the least vulnerable parts of the population, namely young sexually active adults. We will develop methods for reconstructing past populations with uncertainty from fragmentary data. We will produce publicly available software for implementing the new methods.
Every two years, the United Nations produces projections of the populations of all countries, which are widely used by international organizations, governments, the private sector and researchers. The UN's current projections are deterministic, but assessing uncertainty about future population is important for policy-making and other purposes. We propose to develop a fully probabilistic population methodology which will be applicable to all countries, and will also be useful for assessing global concerns, such as climate change and progress towards the Millenium Development Goals.
|Hernández, Belinda; Raftery, Adrian E; Pennington, Stephen R et al. (2018) Bayesian Additive Regression Trees using Bayesian Model Averaging. Stat Comput 28:869-890|
|Sharrow, David J; Godwin, Jessica; He, Yanjun et al. (2018) Probabilistic population projections for countries with generalized HIV/AIDS epidemics. Popul Stud (Camb) 72:1-15|
|Scrucca, Luca; Raftery, Adrian E (2018) clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R. J Stat Softw 84:|
|Godwin, Jessica; Raftery, Adrian E (2017) Bayesian projection of life expectancy accounting for the HIV/AIDS epidemic. Demogr Res 37:1549-1610|
|Raftery, Adrian E; Zimmer, Alec; Frierson, Dargan M W et al. (2017) Less Than 2 °C Warming by 2100 Unlikely. Nat Clim Chang 7:637-641|
|Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao et al. (2017) fastBMA: scalable network inference and transitive reduction. Gigascience 6:1-10|
|McCormick, Tyler H; Lee, Hedwig; Cesare, Nina et al. (2017) Using Twitter for Demographic and Social Science Research: Tools for Data Collection and Processing. Sociol Methods Res 46:390-421|
|Baraff, Aaron J; McCormick, Tyler H; Raftery, Adrian E (2016) Estimating uncertainty in respondent-driven sampling using a tree bootstrap method. Proc Natl Acad Sci U S A 113:14668-14673|
|Scrucca, Luca; Fop, Michael; Murphy, T Brendan et al. (2016) mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J 8:289-317|
|Friel, Nial; Rastelli, Riccardo; Wyse, Jason et al. (2016) Interlocking directorates in Irish companies using a latent space model for bipartite networks. Proc Natl Acad Sci U S A 113:6629-34|
Showing the most recent 10 out of 56 publications